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Economics and Finance

D-Index
41
Citations
14629
World Ranking
2117
National Ranking
1220

Overview

Ingmar R. Prucha is affiliated with the University of Maryland, College Park in the United States. Their research primarily focuses on the field of Economics, Econometrics, and Finance, with a particular emphasis on spatial and panel data analysis.

The subfields of study that Ingmar R. Prucha has extensively contributed to include Economics and Econometrics, Education, Sociology and Political Science, Management Science and Operations Research, and Statistics and Probability.

Key topics covered in their research are:

  • Spatial and Panel Data Analysis
  • Regional Economics and Spatial Analysis
  • School Choice and Performance
  • Media Influence and Politics
  • Housing Market and Economics
  • Fiscal Policy and Economic Growth
  • Demographic Modeling and Climate Adaptation

Their recent papers, reflecting a focus on spatial econometrics and network interactions, are:

  • SIMULTANEOUS EQUATIONS MODELS WITH HIGHER-ORDER SPATIAL OR SOCIAL NETWORK INTERACTIONS, 2022, Econometric Theory
  • Dynamic Spatial Panel Models: Networks, Common Shocks, and Sequential Exogeneity, 2020, Econometrica
  • Efficient peer effects estimators with group effects, 2023, Journal of Econometrics
  • Refined GMM estimators for simultaneous equations models with network interactions, 2023, Empirical Economics
  • Refined GMM Estimators for Simultaneous Equations Models with Network Interactions, 2022, SSRN Electronic Journal

Ingmar R. Prucha frequently collaborates with several scholars in their research:

  • Peter Egger
  • Guido M. Kuersteiner
  • Ying Zeng
  • David M. Drukker

Their work has been published in various academic venues, with multiple contributions to:

  • Econometric Theory
  • Journal of Econometrics
  • arXiv (Cornell University)
  • Econometrica
  • Empirical Economics

Best Publications

  • A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances

    Harry H. Kelejian;Ingmar R. Prucha

  • A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model

    Harry H. Kelejian;Ingmar R. Prucha

  • Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances

    Harry H. Kelejian;Ingmar R. Prucha

  • Panel data models with spatially correlated error components

    Mudit Kapoor;Harry H. Kelejian;Ingmar R. Prucha

  • On the Asymptotic Distribution of the Moran I Test Statistic with Applications

    Harry H. Kelejian;Ingmar R. Prucha

  • Estimation of simultaneous systems of spatially interrelated cross sectional equations

    Harry H. Kelejian;Ingmar R. Prucha

  • HAC estimation in a spatial framework

    Harry H. Kelejian;Ingmar R. Prucha

  • A SPATIAL CLIFF‐ORD‐TYPE MODEL WITH HETEROSKEDASTIC INNOVATIONS: SMALL AND LARGE SAMPLE RESULTS*

    Irani Arraiz;David M. Drukker;Harry H. Kelejian;Ingmar R. Prucha

  • ESTIMATION OF THE DEPRECIATION RATE OF PHYSICAL AND R&D CAPITAL IN THE U.S. TOTAL MANUFACTURING SECTOR

    M. Ishaq Nadiri;Ingmar R. Prucha

  • Dynamic Nonlinear Econometric Models: Asymptotic Theory

    Jurgen Franke;Benedikt M. Potscher;Ingmar R. Prucha

  • On Two-Step Estimation of a Spatial Autoregressive Model with Autoregressive Disturbances and Endogenous Regressors

    David M. Drukker;Peter Egger;Ingmar R. Prucha

  • Maximum Likelihood and Generalized Spatial Two-Stage Least-Squares Estimators for a Spatial-Autoregressive Model with Spatial-Autoregressive Disturbances:

    David M. Drukker;Ingmar R. Prucha;Rafal Raciborski

  • Dynamic Nonlinear Econometric Models

    Benedikt M. Pötscher;Ingmar R. Prucha

  • Central Limit Theorems and Uniform Laws of Large Numbers for Arrays of Random Fields.

    Nazgul Jenish;Ingmar R. Prucha

  • Specification and Estimation of Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances

    Harry H. Kelejian;Ingmar R. Prucha;Ingmar R. Prucha

  • INSTRUMENTAL VARIABLE ESTIMATION OF A SPATIAL AUTOREGRESSIVE MODEL WITH AUTOREGRESSIVE DISTURBANCES: LARGE AND SMALL SAMPLE RESULTS

    Harry H. Kelejian;Ingmar R. Prucha;Yevgeny Yuzefovich

  • Creating and managing spatial-weighting matrices with the spmat command

    David M. Drukker;Hua Peng;Ingmar R. Prucha;Rafal Raciborski

  • A command for estimating spatial-autoregressive models with spatial-autoregressive disturbances and additional endogenous variables

    David M. Drukker;Ingmar R. Prucha;Rafal Raciborski

  • Finite sample properties of estimators of spatial autoregressive models with autoregressive disturbances

    Debabrata Das;Harry H. Kelejian;Ingmar R. Prucha

  • 2SLS and OLS in a spatial autoregressive model with equal spatial weights

    Harry H. Kelejian;Ingmar R. Prucha

  • Estimation of the Depreciation Rate of Physical and R&D Capital in the U.S. Total Manufacturing Sector

    M.I. Nadiri;Ingmar Prucha

Frequent Co-Authors

M. Ishaq Nadiri
M. Ishaq Nadiri New York University
Dilip B. Madan
Dilip B. Madan University of Maryland, College Park
Pierre Mohnen
Pierre Mohnen Maastricht University
Peter Egger
Peter Egger ETH Zurich
Badi H. Baltagi
Badi H. Baltagi Syracuse University

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